DSE
A Dynamic Systems Estimation library.
Paul Gilbert, Bank of Canada
Copyright 1993, 1994, 1995, 1996, Bank of Canada.
Copyright 1997, Paul Gilbert.
The user of this software has the right to use, reproduce and distribute it.
Bank of Canada makes no warranties with respect to the software or its
fitness for any particular purpose. The software is distributed by the Bank
of Canada solely on an "as is" basis. By using the software, user agrees to
accept the entire risk of using this software.
See also the
Brief User's Guide for a general description of the use of this library. This is available as a postscript
or as a pdf file.
Table of Contents
General Description
Index to Functions by Name
Index to Functions by Title
Main Data Objects
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General Description
This is a library of functions for time series modeling. The library works with the S (Splus) and R languages. The functions
in this library are designed for estimating and converting among
various representations of time series models. The library
supports multivariate ARMA and state space models. The
implementation is done using classes and methods, so other model
representations can be added fairly easily. For more details on the
representation of models see the help for objects TSmodel and
TSestModel. For details of the underlying theory and examples
of the capabilities of the system see "State Space and ARMA Models: An
Overview of the Equivalence", P. Gilbert, Bank of Canada working paper
93-4. This is available from the Bank of Canada, or at the same location
as the software (ftp.bank-banque-canada.ca) or by the WWW as a postscript file
or as an Adobe pdf file.
Examples of
the use of several functions can be seen in the functions example.tests
and BOC.paper.tests.
There are also functions for forecasting with the models and for
evaluating the performance of forecasting models. Finally, there are
functions for evaluating model estimation techniques.
In addition to this help facility there is a document called a "Brief
User's Guide" which explains, with examples, the use of the main
functions in the library. This is available in a
postscript
or as a pdf file.
The functions described in the
guide should work fairly reliably, however, many of the functions
described in this help facility are still under development and may not
work. In addition, there may be functions described in the help
facility for which the code has not yet been included with the
software. This is a compromise which allows me to make the software
available with minimum effort. This software is not a commercial
product. It is the by-product of an ongoing research effort.
Constructive suggestions and comments are welcomed. I can be reached at
pgilbert@bank-banque-canada.ca or by phone at (613) 782-7346.
Main Data Objects
TSdata.object
TSdata
time series data object
Description
Class "TSdata" of time series data objects for use with TSmodels.
Generation
This class of objects is returned by the function TSdata or can be
built according to the description below.
Functions in this library which use data pass it through the function
TSdata (which may not do much other than check that the data has class
TSdata).
Methods
The TSdata class of objects has methods for the generic
functions print, plot, periods, start, end, ..., test.equal, series.names,
input.series.names, output.series.names
Also, the function is.TSdata is supported.
Inheritance
Other data classes inherit from the class TSdata.
Structure
Objects are a list with class the most general class "TSdata". The
native form for this library has elements input and
output. Any other elements are ignored. input and
output are matrices (or tframe or time series matrices) of the
input and output data, with each series in a column. TSPADIdata
objects inherit from this class but have a somewhat different
structure. TSPADIdata makes it possible to retrieve data from
an external database when it is needed. These subclass objects do not
contain the actual data, but only the names of the series and the data
base where they are located. The function set.data can be used to set
up an object of class c("TSPADIdata", TSdata").
See Also
Return to Table of Contents
TSmodel.object
TSmodel
Time Series Models
Description
Class "TSmodel" of time series model objects.
Generation
This class of objects is returned by estimation methods or can be built according to the description for specific sub-class (eg "ARMA", "SS", "troll").
Methods
The TSmodel class of objects has methods for the generic functions
print, test.equal, series.names, input.series.names,
output.series.names, l, roots, stability,
forecast, feather.forecasts, horizon.forecasts,
simulate, monte.carlo.simulations
Also, the function is.TSmodel and the functions to.SS, to.ARMA, to.troll are supported.
Inheritance
Other model classes inherit from the class TSmodel.
Structure and Details
See Also
Return to Table of Contents
TSestModel.object
TSestModel
estimated time series model object
Description
Class of object containing a time series model, data, and
estimation information.
Details
This class of objects contains a time series model (TSmodel),
data (TSdata), and information obtained by evaluating the model
with the data. It is a list with
three elements (and optionally more) which are also lists:
- $data
-
is a list of data as described in TSdata.
- $model
-
list of class TSmodel as described in TSmodel.
- $estimates
-
is a list of:
- $like
-
The negative log likelihood function value ( a vector of the total, constant, the det part, and the cov part)
- $cov
-
The estimated residual covariance.
- $pred
-
The one step ahead predictions (see predictT below). These are
aligned with $data$output so that
residuals = estimates$pred[1:sampleT,]-$data$output[1:sampleT,]
- $sampleT
-
The end of the period (starting from 1) for which $data$output
is used for calculating one step ahead predictions.
- $predictT
-
The end of the period for which the model is simulated.
sampleT must be less than or equal predictT. If predictT is
greater than sampleT then each step ahead beyond sampleT is
based on the prediction of the previous step and not
corrected by the prediction error.
- $filter (optional)
-
is a list of
- $state (optional)
-
The one step ahead (filter) estimate of the state E[z(t)|y(t-1), u(t)].
Note: In the case where there is no input u this corresponds to what
would usually be called the predicted state - not the filtered state.
- $track (optional)
-
The estimated state tracking error P(t|t-1). Again note, this corresponds
to the predicted tracking eror not the filtered tracking error.
This is NULL for innovations models.
- $smooth (optional)
-
is a list of
- $state (optional)
-
The smoother (two sided filter) estimate of the state E[z(t)| sampleT].
- $track (optional)
-
The smoothed estimate of the state tracking error P(t|sampleT).
This is NULL for innovations models.
See Also